## Demystifying Beta

It has often been said that “the market loves certainty.”  Most investors (excluding those who seek to capitalize on volatility) would love it if stocks grew in a nice, linear way that was easy to predict and explain.  Alas, stocks don’t do that. They grow in an up and down pattern that is reminiscent of an EKG readout. All that up and down movement overwhelms the brain, and makes it hard to figure out what is going on over the long run.  Since we can’t get stocks to grow in value as a nice, elegant linear function, we tend to look at trends.

On graphs, we can often use lines to show what the trend of a particular stock’s value is over time.  One particular method of doing this is a statistical technique called linear regression.  It essentially takes the average of all the ups and downs and draws a line based on those averages.   You could do the same thing with a ruler by “eyeballing it,” but the results wouldn’t be as precise as the trend line and associated equation that is mathematically generated by a computer.

That last line may have made you cringe a little; I used the words “mathematically” and “equation” in the same sentence.  If you had flashbacks to your high school algebra class, I apologize. But you needn’t be afraid; all of the math is done by computers these days.  All you need to remember from algebra class is that equations can be shown as a line on a graph. Regression analysis capitalizes on this idea in predicting the average movement of data points (stock prices) that don’t move in a nice, straight line like those homework problems from algebra class.  Regression analysis has gotten a bad reputation because of its association with math. Try to forget that; regression is a very useful tool for the investor. All the hard work is done behind the scenes. All you have to do is interpret the results. There are very easy rules of thumbs for interpreting that information.  Feel free to write those down; this isn’t algebra class, and you can’t get in trouble for cheating.

If you were to ask an economist, she would probably say something like “a particular stock’s beta is calculated by dividing the covariance the stock’s returns and the returns of a specified benchmark by the variance of the benchmark’s returns over a specified period.”  My guess is that you didn’t find that very helpful. Let me break it down for you; it’s an easy concept to grasp once we translate the statistical jargon into trader jargon. When we measure anything (such as a stock price) over time and we get different results, we call that thing a variable as opposed to a constant.  Stocks are certainly variable!

That movement of the measurement from value to value is called variation.  Statisticians measure this variability with a number called variance (closely related to standard deviation).  Simply put, variance is a particular statistic that measures the variation in something that varies, such as a stock price.  In the case of stock prices, low variability (as measured by variance) means that the stock’s price doesn’t move much. A high variance means that the stock’s price is bouncing around all over the place.  Traders don’t often use the word variability; they talk about the amount of movement in a stock’s price in terms of volatility.  It may not be precise, but you will probably be okay thinking of variance as a measure of volatility.

Enter the idea of covariance.  As you’d expect, “co” is a prefix meaning “together.”  So the idea of covariation is the idea that two measurements will vary together and, if we generate a scatterplot, the dots will form a line.  For example, we’d expect a high degree of covariance between a stock’s market price and its price to earnings ratio. If the PE ratio was the only factor in determining stock prices, then all of the dots would fall on the line perfectly.  Statisticians would refer to this is a bivariate (meaning two variables) problem, because there are only two variables being considered.

Stock prices are a multivariate (meaning many variables) problem. There are dozens of potential factors that influence stock prices, and only some of them are quantifiable (If this weren’t the case, I could come up with an equation to model future growth and have retired already).  Note that the idea of covariance is conceptually identical to the idea of correlation.

So, the big idea of regression analysis is to demonstrate as precisely as possible how two things systematically vary together.  We can apply this idea to see how much the variability (volatility) of a particular stock matches the variability (volatility) of a benchmark.  That is what Beta is. While any benchmark can be plugged into the equation, most often the variance of the S&P 500 is used with stock prices. Beta, then, is just a ratio of the volatility of a particular stock and the volatility of the S&P 500.  The math tweaks (standardizes) the results for easy interpretation. A Beta of 1.0 indicates that the particular stock you are evaluating moves precisely with the benchmark—it goes up and down exactly as does the S&P 500. A Beta less than 1.0 suggests that (at least in the past) the stock was less volatile than the S&P.  A Beta above 1.0 suggest that the stock is more volatile than the S&P.

Consider the idea that volatility is only a bad thing when it goes against the way you bet. If you are long in a stock, and it shoots past the S&P 500 average, then you picked an awesome stock! If it, however, plummets below the level of the S&P 500, then you are a much bigger loser than the overall market.  Beta assesses volatility objectively. What you ultimately decide to do with that information depends on how risk averse you are. Super conservative investors that are willing to tolerate very little risk will look for stocks with a Beta less than one, such as many utility stocks (often referred to as bond market equivalent stocks).

For example, as of this writing, the Beta for Procter & Gamble Co. (PG) is 0.6. Risk takers seeking big rewards will often look for stocks with a high Beta and the accompanying possibility of big returns—and huge losses.  Note that Beta is neutral as to evaluating great returns or terrible returns. As of this writing, the Beta for Goldman Sachs Group Inc. (GS) is 1.6. Owners of GS are springing for the good stuff this Christmas! Apple Inc. (AAPL), on the other hand, has a Beta of 1.3 and that volatility is unwelcome by investors.

To really get any useful information from Beta, there must be a correlation between the stock you are evaluating and the benchmark used in the computations.  To evaluate this, we can turn to another byproduct of regression analysis known lovingly by economists as R-squared. Think of R-squared as a percentage of covariation.  The closer to 100 you get, the more the stock traces the benchmark’s performance. The closer to zero you get, the less correlation there is between your stock and the benchmark.

More advanced measures have been developed since the advent of computer technology, such as the Sharpe Ratio. The bottom line is that Beta and other measures of volatility are useful tools (among many) that you can use to help you pick a stock that meets your investment needs and form realistic appraisals of how high it can go, and how low it can sink.

## Safety Trade is Getting Dangerous

The Russell 2000 small-cap index is up nearly 11% so far this year, while the venerable old S&P 500 is up only around 5%.  The disparity is due largely to the trade war, and investors have bought the stocks of small capitalization American companies with great vigor.  The normal correlation between markets has been tossed out, it seems, and the relationship has turned inverse. Anytime the S&P 500 looks weak, the Russell 2000 has a good day.  Investors are forgetting a few things about business economics, and that is a very dangerous mistake to my way of thinking. One thing we need to remember is that small companies have supply chains just like large companies, and these are rather limited in comparison.

We are essentially blind when it comes to knowing where what companies get what materials.  If a small knife company in Wisconson needs a certain type of steel, the can’t be too picky where they get it, and they don’t have the bargaining power to drive the price down.  They will pay the market price. If GM and Ford are having problems with the plentiful steel that car parts are generally made with, we can only imagine the trouble that small manufacturers that require specialized materials are having.  What percentage of the small-cap supply chain is dependent on our foes in the trade war? Estimates abound, but these are largely derived using the SWAG method and are no basis for careful analysis.

Another key issue is margin expansion due to increased demand.  If investors are flooding into small-cap stocks, there aren’t enough to go round.  This drives prices up substantially, and those already in the space have a great year (so far).   As much as it pains me to admit, the vicissitudes of politics do have a huge impact on the valuation of companies, both large and small.  With the 2018 midterm elections on the horizon, the political pressure is on to demonstrate to the world that the GOP is indeed Making America Great Again.

Regardless of how good the deals we can get really are, I predict a massive streak of deal signing and a commensurate amount of back patting and acclaim that the deals are great.   Democrats will attack the deals as smoke and mirrors. The truth, as always, will be somewhere in the middle. Regardless o how good the deals are, it will have a calming effect on Wall Street as the uncertainty level drops.  When that happens, traders will see that the small caps have run, and there will likely be a rotation back into large-cap multinationals that have been hurt by the trade war.

I don’t mean to retract my previous predictions that we are nearing a downturn in the broader economy and a big scary pullback in equity prices.  I do, however, agree with Ray Dalio’s timeline and think it is a bit premature to start yelling that the sky is falling. There is a high probability that we’ll see a bit more euphoria and another big rotation before a broad downturn occurs.  I think the next big boom will be back into the out of favor sectors damaged by the trade war, so the industrials and emerging markets will have a few days in the sun.

Regardless of where the money goes, it will come out of small caps. The more I hear watercooler talk of getting into the small-cap space the more I think that the space is overbought.  I recommend getting out of the space and looking toward the beat up sectors, especially emerging markets. I also like Canada and the financials at this stage.

With the FANG earning season in shambles, there may be a sale in tech in the near future.  I would wait for a massive pullback before entering that space as it has flown to amazing heights.  Big moves from recent values don’t necessarily reflect meaningful moves relative to fair valuations.  I am very wary of the upward move in Amazon as the EPS move was truly spectacular, but revenues were essentially flat.  Letting large sums flow down to the bottom line doesn’t tell a growth story, it tells a story of maturity. Amazon may be the retail business equivalent of a bulldozer, and we need to remember that bulldozers aren’t nimble.

I recently closed out my leveraged biotech position, and am holding financials and energy.  I’m short both the Russell 2000 and the S&P 500. I’m also sitting on a lot of cash, waiting on that sale.

You may also be interest in a section of my book entitled Take Some Off the Table.